This file is mainly focus on the prelimianry selected sites by Beni that do not have the early GPP estimation
step1: tidy the table for GPP simulation vs GPP obs sites
step2: adopt the same way to separate out the model early simulation period as for the sites with early GPP estimation
library(kableExtra)
library("readxl")
table.path<-"C:/Users/yluo/Desktop/CES/Data_for_use/"
my_data <- read_excel(paste0(table.path,"Info_Table_about_Photocold_project.xlsx"), sheet = "Sites_without_earlyGPPest")
# my_data %>%
# kbl(caption = "Summary of sites with early GPP estimation") %>%
# kable_paper(full_width = F, html_font = "Cambria") %>%
# scroll_box(width = "500px", height = "200px") #with a scroll bars
my_data %>%
kbl(caption = "Summary of sites with early GPP estimation") %>%
kable_classic(full_width = F, html_font = "Cambria")
| SiteName | Delay_status | Long. | Lat. | Period | PFT | Clim. | N | Calib. | Avai.analyzed.years-spring | Avai.site-years-spring | Avai.analyzed.years-springawinter | Avai.site-years-springawinter | Reference |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| IT-Ren | No | 11.43 | 46.59 | 1998-2013 | ENF | Dfc | 3405 | Y | 2002-2003,2005-2013 | 11 | 2002-2003,2005-2013 | 11 | Montagnani et al. (2009) |
| RU-Ha1 | No | 90.00 | 54.73 | 2002-2004 | GRA | Dfc | 567 | NA | no early years (2002-2004 lack early doy) | 0 | no early years (2002-2004 lack early doy) | 0 | Belelli Marchesini et al. (2007) |
| BE-Vie | No | 6.00 | 50.31 | 1996-2014 | MF | Cfb | 4910 | Y | 2000-2014 | 15 | 2000-2014 | 15 | Aubinet et al. (2001) |
| CH-Cha | No | 8.41 | 47.21 | 2005-2014 | GRA | Cfb | 2944 | NA | 2006-2008,2010-2014 | 8 | 2006-2008,2010-2014 | 8 | Merbold et al. (2014) |
| CH-Lae | No | 8.37 | 47.48 | 2004-2014 | MF | Cfb | 3551 | Y | 2005-2014(2004 lack early doy) | 10 | 2005-2014(2004 lack early doy) | 10 | Etzold et al. (2011) |
| CH-Oe1 | No | 7.73 | 47.29 | 2002-2008 | GRA | Cfb | 2184 | Y | 2002-2008 | 7 | 2002-2008 | 7 | Ammann et al. (2009) |
| DE-Gri | No | 13.51 | 50.95 | 2004-2014 | GRA | Cfb | 3642 | Y | 2004-2014 | 11 | 2004-2014 | 11 | Prescher et al. (2010) |
| DE-Obe | No | 13.72 | 50.78 | 2008-2014 | ENF | Cfb | 2260 | Y | 2008-2014 | 7 | 2008-2014 | 7 | NA |
| DE-RuR | No | 6.30 | 50.62 | 2011-2014 | GRA | Cfb | 1227 | Y | 2012-2014 | 3 | 2012-2014 | 3 | Post et al. (2015) |
| DE-Tha | No | 13.57 | 50.96 | 1996-2014 | ENF | Cfb | 5141 | Y | 2000-2014 | 15 | 2000-2014 | 15 | Grünwald and Bernhofer (2007) |
| NL-Hor | No | 5.07 | 52.24 | 2004-2011 | GRA | Cfb | 2188 | Y | 2005,2007-2011 | 6 | 2005,2007-2010 | 5 | Jacobs et al. (2007) |
| NL-Loo | No | 5.74 | 52.17 | 1996-2013 | ENF | Cfb | 4671 | Y | 2000-2013 | 14 | 2000-2013 | 14 | Moors (2012) |
| Sum | NA | NA | NA | NA | NA | NA | 36690 | NA | NA | 107 | NA | 106 | NA |
## [1] 5
(2) For Cfb:for GRA, MF and ENF sites
## [1] 8
## [1] 7
## [1] 11
## [1] 3
## [1] 6
- Cfb-ENF (3 site)
## [1] 7
## [1] 15
## [1] 14
```
Step1: normlization for all the years in one site
#normalized the gpp_obs and gpp_mod using the gpp_max(95 percentile of gpp)
Step 2:Determine the green-up period for each year(using spline smoothed values):
#followed analysis is based on the normlized “GPP_mod”time series(determine earlier sos)
using the normalized GPP_mod to determine sos,eos and peak of the time series (using the threshold, percentile 10 of amplitude, to determine the sos and eos in this study). We selected the GPP_mod to determine the phenophases as genearlly we can get earlier sos compared to GPP_obs–> we can have larger analysis period
Step 3:rolling mean of GPPobs and GPPmod for data for all the years(moving windown:5,7,10, 15, 20days)
also for the data beyond green-up period–> the code of this steps moves to second step
Step 4:Fit the Guassian norm distribution for residuals beyond the green-up period
The reason to conduct this are: we assume in general the P-model assume the GPP well outside the green-up period (compared to the observation data).
But in practise, the model performance is not always good beyond the green-up period–>I tested three data range:
[peak,265/366]
DoY[1, sos]& DOY[peak,365/366]
[1,sos] & [eos,365/366]
I found the using the data range c, the distrbution of biase (GPP_mod - GPP_obs) is more close to the norm distribution, hence at end of I used the data range c to build the distribution.
step 5:determine the “is_event” within green-up period
After some time of consideration, I took following crition to determine the “is_event”:
during the growing season period (sos,eos)–>the data with GPP biases bigger than 1.2 SD are classified as the “GPP overestimation points”
For “GPP overstimation points”, thoses are air temparture is less than 10 degrees will be classified as the “is_event”. I selected 10 degree as the crition by referring to the paper Duffy et al., 2021 and many papers which demonstrate the temperature response curve normally from 10 degree (for instance: Lin et al., 2012)
References:
Duffy et al., 2021:https://advances.sciencemag.org/content/7/3/eaay1052
Lin et al., 2012:https://academic.oup.com/treephys/article/32/2/219/1657108
step 6:Evaluation “is_event”–>visualization and stats
visulization
stats: \[ Pfalse = \frac{days(real_{(is-event)})}{days(flagged_{(is-event)})} \]